SYSTEM AND METHOD FOR USER COHORT VALUE PREDICTION
A method, a system, and an article are provided for determining a value for a cohort of users of a client application. An example method includes: obtaining data for a plurality of users of a client application; developing, using the data, a first predictive model to predict a likelihood that a user of the client application will become a payer; developing, using the data, a second predictive model to predict an amount of revenue generated in the client application by the payer; providing the client application to a plurality of new users; using the first predictive model and the second predictive model to predict an amount of revenue generated by a cohort of the new users; and adjusting, based on the predicted revenue for the cohort, a method of acquiring additional users of the client application.
1 . A method, comprising:
obtaining data for a plurality of users of a client application;
developing, using the data, a first predictive model to predict a likelihood that a user of the client application will become a payer;
developing, using the data, a second predictive model to predict an amount of revenue generated in the client application by the payer;
providing the client application to a plurality of new users;
using the first predictive model and the second predictive model to predict an amount of revenue generated by a cohort of the new users; and
adjusting, based on the predicted revenue for the cohort, a method of acquiring additional users of the client application.
2 . The method of claim 1 , wherein the data comprises a record of user activity from before or after installation of the client application.
3 . The method of claim 1 , wherein the data comprises at least one of a user characteristic or a client device characteristic.
4 . The method of claim 1 , wherein the client application comprises a multiplayer online game.
5 . The method of claim 1 , wherein using the first predictive model comprises:
providing, as input to the first predictive model, one or more features for each new user in the plurality of new users,
wherein the one or more features comprise an indication of the new user's activity from before or after the new user began using the client application; and
receiving, as output from the first predictive model, a predicted likelihood that each new user will be a payer in the client application.
6 . The method of claim 1 , wherein using the second predictive model comprises:
providing, as input to the second predictive model, one or more features for each new user in the plurality of new users,
wherein the one or more features for each new user comprise an indication of the new user's activity from before or after the new user began using the client application; and
receiving, as output from the second predictive model, a predicted amount of revenue generated by each new user who becomes a payer in the client application.
7 . The method of claim 1 , wherein using the first predictive model and the second predictive model comprises:
combining predictions from the first predictive model and the second predictive model to predict an amount of revenue generated by each new user who becomes a payer in the client application;
identifying from among the plurality of new users a subset of new users who belong to the cohort; and
determining a total predicted revenue generated by the subset of new users.
8 . The method of claim 1 , wherein the predicted amount of revenue generated by the cohort comprises a prediction for an initial time after the cohort began using the client application.
9 . The method of claim 8 , wherein using the first predictive model and the second predictive model comprises:
extrapolating the prediction for the initial time to a later time using one or more multipliers.
10 . The method of claim 1 , wherein the method of acquiring additional users comprises presenting content related to the client application to a set of prospective additional users.
11 . A system, comprising:
one or more computer processors programmed to perform operations comprising:
obtaining data for a plurality of users of a client application;
developing, using the data, a first predictive model to predict a likelihood that a user of the client application will become a payer;
developing, using the data, a second predictive model to predict an amount of revenue generated in the client application by the payer;
providing the client application to a plurality of new users;
using the first predictive model and the second predictive model to predict an amount of revenue generated by a cohort of the new users; and
adjusting, based on the predicted revenue for the cohort, a method of acquiring additional users of the client application.
12 . The system of claim 11 , wherein the data comprises a record of user activity from before or after installation of the client application.
13 . The system of claim 11 , wherein the client application comprises a multiplayer online game.
14 . The system of claim 11 , wherein using the first predictive model comprises:
providing, as input to the first predictive model, one or more features for each new user in the plurality of new users,
wherein the one or more features comprise an indication of the new user's activity from before or after the new user began using the client application; and
receiving, as output from the first predictive model, a predicted likelihood that each new user will be a payer in the client application.
15 . The system of claim 11 , wherein using the second predictive model comprises:
providing, as input to the second predictive model, one or more features for each new user in the plurality of new users,
wherein the one or more features for each new user comprise an indication of the new user's activity from before or after the new user began using the client application; and
receiving, as output from the second predictive model, a predicted amount of revenue generated by each new user who becomes a payer in the client application.
16 . The system of claim 11 , wherein using the first predictive model and the second predictive model comprises:
combining predictions from the first predictive model and the second predictive model to predict an amount of revenue generated by each new user who becomes a payer in the client application;
identifying from among the plurality of new users a subset of new users who belong to the cohort; and
determining a total predicted revenue generated by the subset of new users.
17 . The system of claim 11 , wherein the predicted amount of revenue generated by the cohort comprises a prediction for an initial time after the cohort began using the client application.
18 . The system of claim 17 , wherein using the first predictive model and the second predictive model comprises:
extrapolating the prediction for the initial time to a later time using one or more multipliers.
19 . The system of claim 11 , wherein the method of acquiring additional users comprises presenting content related to the client application to a set of prospective additional users.
20 . An article, comprising:
a non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more computer processors, cause the one or more computer processors to perform operations comprising:
obtaining data for a plurality of users of a client application;
developing, using the data, a first predictive model to predict a likelihood that a user of the client application will become a payer;
developing, using the data, a second predictive model to predict an amount of revenue generated in the client application by the payer;
providing the client application to a plurality of new users;
using the first predictive model and the second predictive model to predict an amount of revenue generated by a cohort of the new users; and
adjusting, based on the predicted revenue for the cohort, a method of acquiring additional users of the client application.